In this work, we propose a novel consistency-based loss called cycle loss, which is illustrated in Figure REF .
The key motivation of our method is that an agent's future trajectory should be coherent with its past observations, and this coherency should still hold even if we reverse the sequence backward in time.
Motivated by this observation, we propose to add an auxiliary task and loss term in the existing training scheme of the motion forecasting model.
After the model predicts the future trajectory using the history input,
we reverse the predicted future trajectory backward in time and reverse the direction of the road graph, and we feed them back into the prediction model to let it predict the history.
We compute the loss of this auxiliary task as an additional cycle loss term.
Similar to the temporal consistency loss proposed in [1]}, our cycle loss method is generic and can be applied to any motion forecasting model.
